---
language:
- nan
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
- mozilla-foundation/common_voice_15_0
model-index:
- name: Whisper Small Taiwanese
  results: []
metrics:
- cer
pipeline_tag: automatic-speech-recognition
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Whisper Small Taiwanese

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 16.1 and the Common Voice 15.0 datasets.
It achieves the following results on the evaluation set:
- Loss: 0.3658
- Cer: 29.0572

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Cer     | Validation Loss |
|:-------------:|:-----:|:----:|:-------:|:---------------:|
| 0.2295        | 1.13  | 3500 | 32.4577 | 0.4268          |
| 0.179         | 1.29  | 4000 | 32.1109 | 0.4088          |
| 0.161         | 1.45  | 4500 | 30.9241 | 0.3923          |
| 0.1607        | 1.61  | 5000 | 30.1640 | 0.3840          |
| 0.1336        | 1.77  | 5500 | 29.7573 | 0.3783          |
| 0.1258        | 1.93  | 6000 | 29.5906 | 0.3736          |
| 0.0725        | 2.09  | 6500 | 30.3507 | 0.3842          |
| 0.0692        | 2.25  | 7000 | 30.0973 | 0.3776          | 
| 0.0635        | 2.41  | 7500 | 29.5106 | 0.3740          | 
| 0.053         | 2.57  | 8000 | 29.0772 | 0.3706          | 
| 0.0441        | 2.73  | 8500 | 28.5238 | 0.3656          | 
| 0.0427        | 2.89  | 9000 | 29.0572 | 0.3658          | 


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2